Keras model-based spectrum identification and classification method

Abstract

The invention belongs to the technical field of information classification methods, and particularly relates to a Keras model-based spectrum identification and classification method. The method comprises the following steps: 1, establishing a standard sample library for a sample set; 2, randomly dividing the standard sample library into a training sample set and a test sample set; 3, constructing a model; 4, compiling the model; 5, training the model; and 6, outputting a classification result. According to the method, the spectral text file is converted into the two-dimensional matrix, high-precision spectral recognition and classification are carried out by building the classification model, and the model structure can be continuously optimized to improve the classification effect; and the spectrum is classified and recognized through the algorithm, manual intervention is reduced, and the classification efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of information classification methods, and in particular relates to a spectral recognition and classification method based on a keras model. Background technique [0002] At present, the traditional spectral recognition of ground objects is generally based on professional software such as envi. However, with the increase in the amount of data and the complexity of the data, the traditional spectral recognition has been unable to perform classification tasks in the context of complex large-scale data. Moreover, there are many steps, which require more manual intervention, and put forward high requirements for the professional knowledge of spectral classification personnel, so that they cannot be competent in the current background environment of big data. Contents of the invention [0003] The present invention aims at the defects of the prior art. The purpose of the present invention is to provide a machi...

AI Technical Summary

Problems solved by technology

However, with the increase in the amount of data and the increase in data complexity, traditional spectral recognition has been unable to perform classification tasks in the context of complex large-scale data.

Moreover, there are many steps, which require more manual intervention, and put forward high requirements for the professional knowledge of spectral classification personnel, so that they cannot be competent in the current background environment of big data.

Images